{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Packages\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot settings\n",
    "sns.set_context('notebook') \n",
    "sns.set_style('ticks') \n",
    "colours = ['#1F77B4', '#FF7F0E', '#2CA02C', '#DB2728', '#9467BD', '#8C564B', '#E377C2','#7F7F7F', '#BCBD22', '#17BECF']\n",
    "crayon = ['#4E79A7','#F28E2C','#E15759','#76B7B2','#59A14F', '#EDC949','#AF7AA1','#FF9DA7','#9C755F','#BAB0AB']\n",
    "sns.set_palette(colours)\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (9, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer Retention Data\n",
    "\n",
    "This dataset is taken from [Statistical Methods in Customer Relationship Management](http://onlinelibrary.wiley.com/book/10.1002/9781118349212), authored by  V. Kumar and J. Andrew Petersen.\n",
    "\n",
    "**Business objective**: to predict which customers will end the relationship with the business, i.e., which customers will churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Censor</th>\n",
       "      <th>Avg_Ret_Exp</th>\n",
       "      <th>Avg_Ret_Exp_SQ</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Total_Crossbuy</th>\n",
       "      <th>Total_Freq</th>\n",
       "      <th>Total_Freq_SQ</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>89.61</td>\n",
       "      <td>8029.9521</td>\n",
       "      <td>1</td>\n",
       "      <td>30.16</td>\n",
       "      <td>1240</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "      <td>49.89</td>\n",
       "      <td>2489.0121</td>\n",
       "      <td>0</td>\n",
       "      <td>39.80</td>\n",
       "      <td>166</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "      <td>40.70</td>\n",
       "      <td>1656.4900</td>\n",
       "      <td>0</td>\n",
       "      <td>54.93</td>\n",
       "      <td>1016</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "      <td>85.76</td>\n",
       "      <td>7354.7776</td>\n",
       "      <td>0</td>\n",
       "      <td>45.83</td>\n",
       "      <td>122</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "      <td>31.90</td>\n",
       "      <td>1017.6100</td>\n",
       "      <td>0</td>\n",
       "      <td>69.03</td>\n",
       "      <td>313</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Duration  Censor  Avg_Ret_Exp  Avg_Ret_Exp_SQ  Industry  Revenue  \\\n",
       "Customer                                                                     \n",
       "1              500       0        89.61       8029.9521         1    30.16   \n",
       "2              730       1        49.89       2489.0121         0    39.80   \n",
       "3              730       1        40.70       1656.4900         0    54.93   \n",
       "4              340       0        85.76       7354.7776         0    45.83   \n",
       "5              730       1        31.90       1017.6100         0    69.03   \n",
       "\n",
       "          Employees  Total_Crossbuy  Total_Freq  Total_Freq_SQ  Churn  \n",
       "Customer                                                               \n",
       "1              1240               6          16            256      1  \n",
       "2               166               6          10            100      0  \n",
       "3              1016               2          14            196      0  \n",
       "4               122               2          15            225      1  \n",
       "5               313               1           9             81      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('Datasets/CustomerChurn.xls', index_col=[0])\n",
    "data['Churn'] = (data['Censor']==0).astype(int)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "response='Churn'\n",
    "predictors=['Avg_Ret_Exp', 'Revenue', 'Employees', 'Total_Crossbuy', 'Total_Freq', 'Industry']\n",
    "\n",
    "data = data[[response]+predictors] # discarding variables that we will not use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the data into training (80%) and test (20%) sets before proceeding. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "index_train, index_test  = train_test_split(np.array(data.index), stratify=data[response], train_size=0.8, random_state=5)\n",
    "\n",
    "train = data.loc[index_train,].copy()\n",
    "test =  data.loc[index_test,:].copy()\n",
    "\n",
    "y_train = train[response]\n",
    "y_test = test[response]\n",
    "\n",
    "X_train = train[predictors]\n",
    "X_test = test[predictors]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "We start by computing descriptive statistics on the response and predictors.\n",
    "\n",
    "It can be observed that 46% of customers ended the relationship with the business:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Churn</th>\n",
       "      <th>Avg_Ret_Exp</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Total_Crossbuy</th>\n",
       "      <th>Total_Freq</th>\n",
       "      <th>Industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.46</td>\n",
       "      <td>36.10</td>\n",
       "      <td>39.74</td>\n",
       "      <td>671.75</td>\n",
       "      <td>3.40</td>\n",
       "      <td>11.47</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.50</td>\n",
       "      <td>33.01</td>\n",
       "      <td>16.33</td>\n",
       "      <td>468.54</td>\n",
       "      <td>1.54</td>\n",
       "      <td>5.85</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.35</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>10.08</td>\n",
       "      <td>27.86</td>\n",
       "      <td>286.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>24.74</td>\n",
       "      <td>39.94</td>\n",
       "      <td>588.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>57.24</td>\n",
       "      <td>51.69</td>\n",
       "      <td>1025.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>145.16</td>\n",
       "      <td>74.97</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Churn  Avg_Ret_Exp  Revenue  Employees  Total_Crossbuy  Total_Freq  \\\n",
       "count  400.00       400.00   400.00     400.00          400.00      400.00   \n",
       "mean     0.46        36.10    39.74     671.75            3.40       11.47   \n",
       "std      0.50        33.01    16.33     468.54            1.54        5.85   \n",
       "min      0.00         0.04     2.35       4.00            1.00        1.00   \n",
       "25%      0.00        10.08    27.86     286.25            2.00        6.00   \n",
       "50%      0.00        24.74    39.94     588.50            3.00       12.00   \n",
       "75%      1.00        57.24    51.69    1025.50            5.00       17.00   \n",
       "max      1.00       145.16    74.97    1950.00            6.00       21.00   \n",
       "\n",
       "       Industry  \n",
       "count    400.00  \n",
       "mean       0.60  \n",
       "std        0.49  \n",
       "min        0.00  \n",
       "25%        0.00  \n",
       "50%        1.00  \n",
       "75%        1.00  \n",
       "max        1.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use histograms to further explore the univariate distribution of the numerical predictors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_dists\n",
    "\n",
    "plot_dists(train[predictors[:-1]]) # excludes the last variable, since it is binary\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explore the relationship between the predictors and the response, we use univariate logistic regression plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x396 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_logistic_regressions\n",
    "\n",
    "with sns.color_palette(crayon):\n",
    "    plot_logistic_regressions(train[predictors[:-1]], train[response])\n",
    "    plt.show()\n",
    "# avg_ret_exp and employees have good distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class-conditional distributions (densities) accross the predictors are shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_conditional_distributions\n",
    "\n",
    "plot_conditional_distributions(train[predictors[:-1]], y_train, labels=['Retention', 'Churn'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A higher proportion of B2B prospects (50.6%) churned compared to the non-B2B prospects (40.3%):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Industry</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.597</td>\n",
       "      <td>0.494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.403</td>\n",
       "      <td>0.506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Industry      0      1\n",
       "Churn                 \n",
       "0         0.597  0.494\n",
       "1         0.403  0.506"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table=pd.crosstab(train[response], train['Industry'])\n",
    "table = (table/table.sum()).round(3)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "(table.T).plot(kind='bar', alpha=0.8, ax=ax)\n",
    "ax.set_xlabel('Industry')\n",
    "ax.set_ylabel('Proportions')\n",
    "ax.legend_.set_title('Churn')\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree\n",
    "\n",
    "The basic syntax for fitting a classification tree by recursive binary splitting is provided below.  Here we allow the tree to have a maximum depth of 2 in order to facilitate visualisation. We also specify that the minimum number of \"samples\" (i.e. clients) in the terminal nodes is 5. We do this to highlight the importance of explicitly controlling this tuning parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
       "                       max_depth=2, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=5, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=5)\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have two options for displaying Decision Tree diagrams.  \n",
    "\n",
    "**Option 1 (the simpler one)**: display the diagrams externally using <TT>WebGraphviz</TT>.\n",
    "\n",
    "- Open http://www.webgraphviz.com/ in a new browser window\n",
    "\n",
    "- Run the code in the cell below (in your Jupyter notebook), copy the output and paste it into the WebGraphviz' Text Area. Click \"Generate Graph!\" to produce the tree diagram.\n",
    "\n",
    "\n",
    "**Option 2**:  display the diagrams within the Jupyter notebook:\n",
    "\n",
    "To display the diagram within your Jupyter notebook please use <TT>homebrew</TT>, which can be installed via the following links:\n",
    "\n",
    "Mac Users: https://www.howtogeek.com/211541/homebrew-for-os-x-easily-installs-desktop-apps-and-terminal-utilities/) \n",
    "\n",
    "PC Users: https://docs.brew.sh/Homebrew-on-Linux\n",
    "\n",
    "Once homebrew is installed, open up the terminal and type in <TT>brew install graphviz</TT>.  This installs the <TT>graphviz</TT> package, wchich we can use to produce decision tree diagrams.  The corresponding code is provided further below.\n",
    "\n",
    "\n",
    "You should experiment with letting the tree grow more (by changing the value for max_depth) and interpreting the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell can be used to display the tree diagram externally (**Option 1**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digraph Tree {\n",
      "node [shape=box, style=\"rounded\", color=\"black\", fontname=helvetica] ;\n",
      "edge [fontname=helvetica] ;\n",
      "0 [label=\"Avg_Ret_Exp <= 66.77\\nsamples = 400\\nvalue = [214, 186]\\nclass = retention\"] ;\n",
      "1 [label=\"Employees <= 395.0\\nsamples = 323\\nvalue = [209, 114]\\nclass = retention\"] ;\n",
      "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
      "2 [label=\"samples = 132\\nvalue = [51, 81]\\nclass = churn\"] ;\n",
      "1 -> 2 ;\n",
      "3 [label=\"samples = 191\\nvalue = [158, 33]\\nclass = retention\"] ;\n",
      "1 -> 3 ;\n",
      "4 [label=\"Employees <= 1225.5\\nsamples = 77\\nvalue = [5, 72]\\nclass = churn\"] ;\n",
      "0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
      "5 [label=\"samples = 64\\nvalue = [0, 64]\\nclass = churn\"] ;\n",
      "4 -> 5 ;\n",
      "6 [label=\"samples = 13\\nvalue = [5, 8]\\nclass = churn\"] ;\n",
      "4 -> 6 ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# This cell can be used to display the tree diagram externally (Option 1)\n",
    "\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "dot_data = export_graphviz(tree, out_file=None, feature_names=predictors, impurity=False,\n",
    "                           class_names=['retention','churn'], rounded=True) \n",
    "print(dot_data) #copy and paste the output into the WebGraphviz' Text Area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell can be used to display the tree diagram within the Jupyter notebook if you have done all the installations (**Option 2**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " viewBox=\"0.00 0.00 519.50 269.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 265)\">\n",
       "<title>Tree</title>\n",
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       "<text text-anchor=\"middle\" x=\"265.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 400</text>\n",
       "<text text-anchor=\"middle\" x=\"265.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [214, 186]</text>\n",
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       "<title>1</title>\n",
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       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
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       "<text text-anchor=\"middle\" x=\"349.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 77</text>\n",
       "<text text-anchor=\"middle\" x=\"349.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 72]</text>\n",
       "<text text-anchor=\"middle\" x=\"349.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
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       "<!-- 0&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>0&#45;&gt;4</title>\n",
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       "<title>2</title>\n",
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       "<text text-anchor=\"middle\" x=\"56.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 132</text>\n",
       "<text text-anchor=\"middle\" x=\"56.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [51, 81]</text>\n",
       "<text text-anchor=\"middle\" x=\"56.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>1&#45;&gt;2</title>\n",
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       "<title>3</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M239.5,-53C239.5,-53 143.5,-53 143.5,-53 137.5,-53 131.5,-47 131.5,-41 131.5,-41 131.5,-12 131.5,-12 131.5,-6 137.5,0 143.5,0 143.5,0 239.5,0 239.5,0 245.5,0 251.5,-6 251.5,-12 251.5,-12 251.5,-41 251.5,-41 251.5,-47 245.5,-53 239.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"191.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 191</text>\n",
       "<text text-anchor=\"middle\" x=\"191.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [158, 33]</text>\n",
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       "</g>\n",
       "<!-- 1&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>1&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M185.6731,-88.9777C186.4502,-80.6449 187.2854,-71.6903 188.0738,-63.2364\"/>\n",
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       "<text text-anchor=\"middle\" x=\"340.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 64</text>\n",
       "<text text-anchor=\"middle\" x=\"340.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 64]</text>\n",
       "<text text-anchor=\"middle\" x=\"340.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>4&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M346.3269,-88.9777C345.5498,-80.6449 344.7146,-71.6903 343.9262,-63.2364\"/>\n",
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       "<title>6</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M499.5,-53C499.5,-53 423.5,-53 423.5,-53 417.5,-53 411.5,-47 411.5,-41 411.5,-41 411.5,-12 411.5,-12 411.5,-6 417.5,0 423.5,0 423.5,0 499.5,0 499.5,0 505.5,0 511.5,-6 511.5,-12 511.5,-12 511.5,-41 511.5,-41 511.5,-47 505.5,-53 499.5,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"461.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 13</text>\n",
       "<text text-anchor=\"middle\" x=\"461.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 8]</text>\n",
       "<text text-anchor=\"middle\" x=\"461.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>4&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M388.987,-88.9777C399.9336,-79.546 411.8047,-69.3178 422.7111,-59.9208\"/>\n",
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       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7fc5848b25d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "dot_data = export_graphviz(tree, out_file=None, feature_names=predictors, impurity=False,\n",
    "                           class_names=['retention','churn'], rounded=True) \n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render('tree01') # saves tree to a file\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we should find a tree of optimal size for prediction. Unfortunately, <TT>scikit-learn</TT> does not support cost-complexity pruning. As an alternative, we select the depth and minimum node size using the automated [<TT>GridSearchCV</TT>](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) funtion for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found by grid search: {'min_samples_leaf': 5} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "model = DecisionTreeClassifier(criterion='entropy')\n",
    "\n",
    "tuning_parameters = {\n",
    "    'min_samples_leaf': [1,5,10,20,30,40,50],\n",
    "}\n",
    "#enough to just use min to cotrol the size of the tree\n",
    "tree_search = GridSearchCV(model, tuning_parameters, cv= 5 , return_train_score=False)\n",
    "tree_search.fit(X_train, y_train)\n",
    "\n",
    "tree = tree_search.best_estimator_\n",
    "\n",
    "print('Best parameters found by grid search:', tree_search.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell can be used to display the tree diagram externally (**Option 1**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digraph Tree {\n",
      "node [shape=box, style=\"rounded\", color=\"black\", fontname=helvetica] ;\n",
      "edge [fontname=helvetica] ;\n",
      "0 [label=\"Avg_Ret_Exp <= 66.77\\nsamples = 400\\nvalue = [214, 186]\\nclass = retention\"] ;\n",
      "1 [label=\"Employees <= 395.0\\nsamples = 323\\nvalue = [209, 114]\\nclass = retention\"] ;\n",
      "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
      "2 [label=\"Total_Crossbuy <= 3.5\\nsamples = 132\\nvalue = [51, 81]\\nclass = churn\"] ;\n",
      "1 -> 2 ;\n",
      "3 [label=\"Employees <= 238.5\\nsamples = 70\\nvalue = [11, 59]\\nclass = churn\"] ;\n",
      "2 -> 3 ;\n",
      "4 [label=\"samples = 32\\nvalue = [0, 32]\\nclass = churn\"] ;\n",
      "3 -> 4 ;\n",
      "5 [label=\"Total_Freq <= 14.5\\nsamples = 38\\nvalue = [11, 27]\\nclass = churn\"] ;\n",
      "3 -> 5 ;\n",
      "6 [label=\"Industry <= 0.5\\nsamples = 23\\nvalue = [3, 20]\\nclass = churn\"] ;\n",
      "5 -> 6 ;\n",
      "7 [label=\"samples = 8\\nvalue = [3, 5]\\nclass = churn\"] ;\n",
      "6 -> 7 ;\n",
      "8 [label=\"samples = 15\\nvalue = [0, 15]\\nclass = churn\"] ;\n",
      "6 -> 8 ;\n",
      "9 [label=\"Avg_Ret_Exp <= 11.22\\nsamples = 15\\nvalue = [8, 7]\\nclass = retention\"] ;\n",
      "5 -> 9 ;\n",
      "10 [label=\"samples = 5\\nvalue = [1, 4]\\nclass = churn\"] ;\n",
      "9 -> 10 ;\n",
      "11 [label=\"Avg_Ret_Exp <= 26.91\\nsamples = 10\\nvalue = [7, 3]\\nclass = retention\"] ;\n",
      "9 -> 11 ;\n",
      "12 [label=\"samples = 5\\nvalue = [5, 0]\\nclass = retention\"] ;\n",
      "11 -> 12 ;\n",
      "13 [label=\"samples = 5\\nvalue = [2, 3]\\nclass = churn\"] ;\n",
      "11 -> 13 ;\n",
      "14 [label=\"Avg_Ret_Exp <= 48.365\\nsamples = 62\\nvalue = [40, 22]\\nclass = retention\"] ;\n",
      "2 -> 14 ;\n",
      "15 [label=\"Total_Crossbuy <= 5.5\\nsamples = 51\\nvalue = [39, 12]\\nclass = retention\"] ;\n",
      "14 -> 15 ;\n",
      "16 [label=\"Avg_Ret_Exp <= 7.39\\nsamples = 39\\nvalue = [27, 12]\\nclass = retention\"] ;\n",
      "15 -> 16 ;\n",
      "17 [label=\"samples = 8\\nvalue = [8, 0]\\nclass = retention\"] ;\n",
      "16 -> 17 ;\n",
      "18 [label=\"Avg_Ret_Exp <= 28.715\\nsamples = 31\\nvalue = [19, 12]\\nclass = retention\"] ;\n",
      "16 -> 18 ;\n",
      "19 [label=\"Revenue <= 44.26\\nsamples = 23\\nvalue = [11, 12]\\nclass = churn\"] ;\n",
      "18 -> 19 ;\n",
      "20 [label=\"Employees <= 264.5\\nsamples = 13\\nvalue = [3, 10]\\nclass = churn\"] ;\n",
      "19 -> 20 ;\n",
      "21 [label=\"samples = 5\\nvalue = [2, 3]\\nclass = churn\"] ;\n",
      "20 -> 21 ;\n",
      "22 [label=\"samples = 8\\nvalue = [1, 7]\\nclass = churn\"] ;\n",
      "20 -> 22 ;\n",
      "23 [label=\"Total_Freq <= 11.5\\nsamples = 10\\nvalue = [8, 2]\\nclass = retention\"] ;\n",
      "19 -> 23 ;\n",
      "24 [label=\"samples = 5\\nvalue = [3, 2]\\nclass = retention\"] ;\n",
      "23 -> 24 ;\n",
      "25 [label=\"samples = 5\\nvalue = [5, 0]\\nclass = retention\"] ;\n",
      "23 -> 25 ;\n",
      "26 [label=\"samples = 8\\nvalue = [8, 0]\\nclass = retention\"] ;\n",
      "18 -> 26 ;\n",
      "27 [label=\"samples = 12\\nvalue = [12, 0]\\nclass = retention\"] ;\n",
      "15 -> 27 ;\n",
      "28 [label=\"Total_Crossbuy <= 4.5\\nsamples = 11\\nvalue = [1, 10]\\nclass = churn\"] ;\n",
      "14 -> 28 ;\n",
      "29 [label=\"samples = 6\\nvalue = [0, 6]\\nclass = churn\"] ;\n",
      "28 -> 29 ;\n",
      "30 [label=\"samples = 5\\nvalue = [1, 4]\\nclass = churn\"] ;\n",
      "28 -> 30 ;\n",
      "31 [label=\"Total_Crossbuy <= 2.5\\nsamples = 191\\nvalue = [158, 33]\\nclass = retention\"] ;\n",
      "1 -> 31 ;\n",
      "32 [label=\"Employees <= 1119.0\\nsamples = 64\\nvalue = [40, 24]\\nclass = retention\"] ;\n",
      "31 -> 32 ;\n",
      "33 [label=\"Employees <= 698.0\\nsamples = 44\\nvalue = [20, 24]\\nclass = churn\"] ;\n",
      "32 -> 33 ;\n",
      "34 [label=\"Total_Crossbuy <= 1.5\\nsamples = 13\\nvalue = [1, 12]\\nclass = churn\"] ;\n",
      "33 -> 34 ;\n",
      "35 [label=\"samples = 8\\nvalue = [0, 8]\\nclass = churn\"] ;\n",
      "34 -> 35 ;\n",
      "36 [label=\"samples = 5\\nvalue = [1, 4]\\nclass = churn\"] ;\n",
      "34 -> 36 ;\n",
      "37 [label=\"Total_Crossbuy <= 1.5\\nsamples = 31\\nvalue = [19, 12]\\nclass = retention\"] ;\n",
      "33 -> 37 ;\n",
      "38 [label=\"samples = 9\\nvalue = [3, 6]\\nclass = churn\"] ;\n",
      "37 -> 38 ;\n",
      "39 [label=\"Revenue <= 45.45\\nsamples = 22\\nvalue = [16, 6]\\nclass = retention\"] ;\n",
      "37 -> 39 ;\n",
      "40 [label=\"Avg_Ret_Exp <= 18.83\\nsamples = 13\\nvalue = [8, 5]\\nclass = retention\"] ;\n",
      "39 -> 40 ;\n",
      "41 [label=\"samples = 7\\nvalue = [3, 4]\\nclass = churn\"] ;\n",
      "40 -> 41 ;\n",
      "42 [label=\"samples = 6\\nvalue = [5, 1]\\nclass = retention\"] ;\n",
      "40 -> 42 ;\n",
      "43 [label=\"samples = 9\\nvalue = [8, 1]\\nclass = retention\"] ;\n",
      "39 -> 43 ;\n",
      "44 [label=\"samples = 20\\nvalue = [20, 0]\\nclass = retention\"] ;\n",
      "32 -> 44 ;\n",
      "45 [label=\"Total_Freq <= 5.5\\nsamples = 127\\nvalue = [118, 9]\\nclass = retention\"] ;\n",
      "31 -> 45 ;\n",
      "46 [label=\"Revenue <= 29.17\\nsamples = 31\\nvalue = [25, 6]\\nclass = retention\"] ;\n",
      "45 -> 46 ;\n",
      "47 [label=\"samples = 5\\nvalue = [2, 3]\\nclass = churn\"] ;\n",
      "46 -> 47 ;\n",
      "48 [label=\"Employees <= 756.0\\nsamples = 26\\nvalue = [23, 3]\\nclass = retention\"] ;\n",
      "46 -> 48 ;\n",
      "49 [label=\"samples = 8\\nvalue = [5, 3]\\nclass = retention\"] ;\n",
      "48 -> 49 ;\n",
      "50 [label=\"samples = 18\\nvalue = [18, 0]\\nclass = retention\"] ;\n",
      "48 -> 50 ;\n",
      "51 [label=\"Avg_Ret_Exp <= 10.535\\nsamples = 96\\nvalue = [93, 3]\\nclass = retention\"] ;\n",
      "45 -> 51 ;\n",
      "52 [label=\"Avg_Ret_Exp <= 6.065\\nsamples = 35\\nvalue = [32, 3]\\nclass = retention\"] ;\n",
      "51 -> 52 ;\n",
      "53 [label=\"samples = 24\\nvalue = [24, 0]\\nclass = retention\"] ;\n",
      "52 -> 53 ;\n",
      "54 [label=\"Employees <= 758.0\\nsamples = 11\\nvalue = [8, 3]\\nclass = retention\"] ;\n",
      "52 -> 54 ;\n",
      "55 [label=\"samples = 6\\nvalue = [3, 3]\\nclass = retention\"] ;\n",
      "54 -> 55 ;\n",
      "56 [label=\"samples = 5\\nvalue = [5, 0]\\nclass = retention\"] ;\n",
      "54 -> 56 ;\n",
      "57 [label=\"samples = 61\\nvalue = [61, 0]\\nclass = retention\"] ;\n",
      "51 -> 57 ;\n",
      "58 [label=\"Employees <= 1225.5\\nsamples = 77\\nvalue = [5, 72]\\nclass = churn\"] ;\n",
      "0 -> 58 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
      "59 [label=\"samples = 64\\nvalue = [0, 64]\\nclass = churn\"] ;\n",
      "58 -> 59 ;\n",
      "60 [label=\"Avg_Ret_Exp <= 84.615\\nsamples = 13\\nvalue = [5, 8]\\nclass = churn\"] ;\n",
      "58 -> 60 ;\n",
      "61 [label=\"samples = 8\\nvalue = [5, 3]\\nclass = retention\"] ;\n",
      "60 -> 61 ;\n",
      "62 [label=\"samples = 5\\nvalue = [0, 5]\\nclass = churn\"] ;\n",
      "60 -> 62 ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "dot_data = export_graphviz(tree, out_file=None, feature_names=predictors, impurity=False,\n",
    "                           class_names=['retention','churn'], rounded=True) \n",
    "print(dot_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell can be used to display the tree diagram within the Jupyter notebook  if you have done all the installations (**Option 2**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<!-- Title: Tree Pages: 1 -->\n",
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       "<text text-anchor=\"middle\" x=\"1382.5\" y=\"-973.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 66.77</text>\n",
       "<text text-anchor=\"middle\" x=\"1382.5\" y=\"-958.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 400</text>\n",
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       "<text text-anchor=\"middle\" x=\"813.5\" y=\"-765.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Total_Crossbuy &lt;= 3.5</text>\n",
       "<text text-anchor=\"middle\" x=\"813.5\" y=\"-750.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 132</text>\n",
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       "<text text-anchor=\"middle\" x=\"813.5\" y=\"-720.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
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       "<!-- 1&#45;&gt;2 -->\n",
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       "<text text-anchor=\"middle\" x=\"1301.5\" y=\"-735.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [158, 33]</text>\n",
       "<text text-anchor=\"middle\" x=\"1301.5\" y=\"-720.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
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       "<!-- 1&#45;&gt;31 -->\n",
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       "<title>3</title>\n",
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       "<text text-anchor=\"middle\" x=\"372.5\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Employees &lt;= 238.5</text>\n",
       "<text text-anchor=\"middle\" x=\"372.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 70</text>\n",
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       "<text text-anchor=\"middle\" x=\"372.5\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
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       "<!-- 2&#45;&gt;3 -->\n",
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       "<title>2&#45;&gt;3</title>\n",
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       "<text text-anchor=\"middle\" x=\"813.5\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 48.365</text>\n",
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       "<title>2&#45;&gt;14</title>\n",
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       "<title>4</title>\n",
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       "<text text-anchor=\"middle\" x=\"233.5\" y=\"-520.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
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       "<!-- 3&#45;&gt;4 -->\n",
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       "<title>3&#45;&gt;4</title>\n",
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       "<text text-anchor=\"middle\" x=\"372.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Total_Freq &lt;= 14.5</text>\n",
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       "<text text-anchor=\"middle\" x=\"372.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
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       "<text text-anchor=\"middle\" x=\"169.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Industry &lt;= 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"169.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 23</text>\n",
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       "<text text-anchor=\"middle\" x=\"169.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
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       "<text text-anchor=\"middle\" x=\"372.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 11.22</text>\n",
       "<text text-anchor=\"middle\" x=\"372.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 15</text>\n",
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       "<text text-anchor=\"middle\" x=\"372.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
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       "<text text-anchor=\"middle\" x=\"49.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8</text>\n",
       "<text text-anchor=\"middle\" x=\"49.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3, 5]</text>\n",
       "<text text-anchor=\"middle\" x=\"49.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>6&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M130.2075,-400.9465C116.6083,-389.1606 101.3915,-375.9726 87.8676,-364.2519\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"89.9748,-361.4466 80.1256,-357.5422 85.3902,-366.7364 89.9748,-361.4466\"/>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>8</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M210,-357.5C210,-357.5 129,-357.5 129,-357.5 123,-357.5 117,-351.5 117,-345.5 117,-345.5 117,-316.5 117,-316.5 117,-310.5 123,-304.5 129,-304.5 129,-304.5 210,-304.5 210,-304.5 216,-304.5 222,-310.5 222,-316.5 222,-316.5 222,-345.5 222,-345.5 222,-351.5 216,-357.5 210,-357.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"169.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 15</text>\n",
       "<text text-anchor=\"middle\" x=\"169.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 15]</text>\n",
       "<text text-anchor=\"middle\" x=\"169.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>6&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M169.5,-400.9465C169.5,-390.2621 169.5,-378.4254 169.5,-367.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"173.0001,-367.5421 169.5,-357.5422 166.0001,-367.5422 173.0001,-367.5421\"/>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M327,-357.5C327,-357.5 252,-357.5 252,-357.5 246,-357.5 240,-351.5 240,-345.5 240,-345.5 240,-316.5 240,-316.5 240,-310.5 246,-304.5 252,-304.5 252,-304.5 327,-304.5 327,-304.5 333,-304.5 339,-310.5 339,-316.5 339,-316.5 339,-345.5 339,-345.5 339,-351.5 333,-357.5 327,-357.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"289.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"289.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 4]</text>\n",
       "<text text-anchor=\"middle\" x=\"289.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>9&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M345.3227,-400.9465C336.2682,-389.6012 326.177,-376.9567 317.0914,-365.5724\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"319.6561,-363.175 310.6827,-357.5422 314.1849,-367.5414 319.6561,-363.175\"/>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>11</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M506,-365C506,-365 369,-365 369,-365 363,-365 357,-359 357,-353 357,-353 357,-309 357,-309 357,-303 363,-297 369,-297 369,-297 506,-297 506,-297 512,-297 518,-303 518,-309 518,-309 518,-353 518,-353 518,-359 512,-365 506,-365\"/>\n",
       "<text text-anchor=\"middle\" x=\"437.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 26.91</text>\n",
       "<text text-anchor=\"middle\" x=\"437.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 10</text>\n",
       "<text text-anchor=\"middle\" x=\"437.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [7, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"437.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>9&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M393.7834,-400.9465C399.2267,-392.2373 405.1484,-382.7626 410.825,-373.6801\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"413.8367,-375.465 416.1688,-365.13 407.9007,-371.755 413.8367,-375.465\"/>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>12</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M379,-253.5C379,-253.5 284,-253.5 284,-253.5 278,-253.5 272,-247.5 272,-241.5 272,-241.5 272,-212.5 272,-212.5 272,-206.5 278,-200.5 284,-200.5 284,-200.5 379,-200.5 379,-200.5 385,-200.5 391,-206.5 391,-212.5 391,-212.5 391,-241.5 391,-241.5 391,-247.5 385,-253.5 379,-253.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"331.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"331.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"331.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 11&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>11&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M402.7916,-296.9465C390.8913,-285.2707 377.5886,-272.219 365.7267,-260.5809\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"368.1419,-258.0473 358.5526,-253.5422 363.2395,-263.0439 368.1419,-258.0473\"/>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>13</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M496,-253.5C496,-253.5 421,-253.5 421,-253.5 415,-253.5 409,-247.5 409,-241.5 409,-241.5 409,-212.5 409,-212.5 409,-206.5 415,-200.5 421,-200.5 421,-200.5 496,-200.5 496,-200.5 502,-200.5 508,-206.5 508,-212.5 508,-212.5 508,-241.5 508,-241.5 508,-247.5 502,-253.5 496,-253.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"458.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"458.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"458.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 11&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>11&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M444.3762,-296.9465C446.5336,-286.2621 448.9237,-274.4254 451.1148,-263.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"454.5919,-264.0371 453.1405,-253.5422 447.7304,-262.6516 454.5919,-264.0371\"/>\n",
       "</g>\n",
       "<!-- 15 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>15</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M792.5,-573C792.5,-573 658.5,-573 658.5,-573 652.5,-573 646.5,-567 646.5,-561 646.5,-561 646.5,-517 646.5,-517 646.5,-511 652.5,-505 658.5,-505 658.5,-505 792.5,-505 792.5,-505 798.5,-505 804.5,-511 804.5,-517 804.5,-517 804.5,-561 804.5,-561 804.5,-567 798.5,-573 792.5,-573\"/>\n",
       "<text text-anchor=\"middle\" x=\"725.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Total_Crossbuy &lt;= 5.5</text>\n",
       "<text text-anchor=\"middle\" x=\"725.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 51</text>\n",
       "<text text-anchor=\"middle\" x=\"725.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [39, 12]</text>\n",
       "<text text-anchor=\"middle\" x=\"725.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>14&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M784.6855,-608.9465C777.0883,-599.968 768.8026,-590.1758 760.9018,-580.8385\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"763.5105,-578.5031 754.3792,-573.13 758.1668,-583.0247 763.5105,-578.5031\"/>\n",
       "</g>\n",
       "<!-- 28 -->\n",
       "<g id=\"node29\" class=\"node\">\n",
       "<title>28</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M968.5,-573C968.5,-573 834.5,-573 834.5,-573 828.5,-573 822.5,-567 822.5,-561 822.5,-561 822.5,-517 822.5,-517 822.5,-511 828.5,-505 834.5,-505 834.5,-505 968.5,-505 968.5,-505 974.5,-505 980.5,-511 980.5,-517 980.5,-517 980.5,-561 980.5,-561 980.5,-567 974.5,-573 968.5,-573\"/>\n",
       "<text text-anchor=\"middle\" x=\"901.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Total_Crossbuy &lt;= 4.5</text>\n",
       "<text text-anchor=\"middle\" x=\"901.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 11</text>\n",
       "<text text-anchor=\"middle\" x=\"901.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 10]</text>\n",
       "<text text-anchor=\"middle\" x=\"901.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;28 -->\n",
       "<g id=\"edge28\" class=\"edge\">\n",
       "<title>14&#45;&gt;28</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M842.3145,-608.9465C849.9117,-599.968 858.1974,-590.1758 866.0982,-580.8385\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"868.8332,-583.0247 872.6208,-573.13 863.4895,-578.5031 868.8332,-583.0247\"/>\n",
       "</g>\n",
       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>16</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M660,-469C660,-469 531,-469 531,-469 525,-469 519,-463 519,-457 519,-457 519,-413 519,-413 519,-407 525,-401 531,-401 531,-401 660,-401 660,-401 666,-401 672,-407 672,-413 672,-413 672,-457 672,-457 672,-463 666,-469 660,-469\"/>\n",
       "<text text-anchor=\"middle\" x=\"595.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 7.39</text>\n",
       "<text text-anchor=\"middle\" x=\"595.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 39</text>\n",
       "<text text-anchor=\"middle\" x=\"595.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [27, 12]</text>\n",
       "<text text-anchor=\"middle\" x=\"595.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 15&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>15&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M682.9331,-504.9465C671.1488,-495.519 658.2432,-485.1946 646.0519,-475.4415\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"648.1577,-472.6439 638.1625,-469.13 643.7848,-478.11 648.1577,-472.6439\"/>\n",
       "</g>\n",
       "<!-- 27 -->\n",
       "<g id=\"node28\" class=\"node\">\n",
       "<title>27</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M797,-461.5C797,-461.5 702,-461.5 702,-461.5 696,-461.5 690,-455.5 690,-449.5 690,-449.5 690,-420.5 690,-420.5 690,-414.5 696,-408.5 702,-408.5 702,-408.5 797,-408.5 797,-408.5 803,-408.5 809,-414.5 809,-420.5 809,-420.5 809,-449.5 809,-449.5 809,-455.5 803,-461.5 797,-461.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"749.5\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 12</text>\n",
       "<text text-anchor=\"middle\" x=\"749.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [12, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"749.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 15&#45;&gt;27 -->\n",
       "<g id=\"edge27\" class=\"edge\">\n",
       "<title>15&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M733.3585,-504.9465C735.8241,-494.2621 738.5557,-482.4254 741.0598,-471.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"744.5366,-472.0731 743.3749,-461.5422 737.7158,-470.499 744.5366,-472.0731\"/>\n",
       "</g>\n",
       "<!-- 17 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>17</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M643,-357.5C643,-357.5 548,-357.5 548,-357.5 542,-357.5 536,-351.5 536,-345.5 536,-345.5 536,-316.5 536,-316.5 536,-310.5 542,-304.5 548,-304.5 548,-304.5 643,-304.5 643,-304.5 649,-304.5 655,-310.5 655,-316.5 655,-316.5 655,-345.5 655,-345.5 655,-351.5 649,-357.5 643,-357.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"595.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8</text>\n",
       "<text text-anchor=\"middle\" x=\"595.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [8, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"595.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 16&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>16&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M595.5,-400.9465C595.5,-390.2621 595.5,-378.4254 595.5,-367.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"599.0001,-367.5421 595.5,-357.5422 592.0001,-367.5422 599.0001,-367.5421\"/>\n",
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       "<!-- 18 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>18</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M830,-365C830,-365 685,-365 685,-365 679,-365 673,-359 673,-353 673,-353 673,-309 673,-309 673,-303 679,-297 685,-297 685,-297 830,-297 830,-297 836,-297 842,-303 842,-309 842,-309 842,-353 842,-353 842,-359 836,-365 830,-365\"/>\n",
       "<text text-anchor=\"middle\" x=\"757.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 28.715</text>\n",
       "<text text-anchor=\"middle\" x=\"757.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 31</text>\n",
       "<text text-anchor=\"middle\" x=\"757.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [19, 12]</text>\n",
       "<text text-anchor=\"middle\" x=\"757.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 16&#45;&gt;18 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>16&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M648.5449,-400.9465C663.6495,-391.2497 680.2324,-380.6039 695.8046,-370.6069\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"697.8116,-373.4777 704.336,-365.13 694.03,-367.5871 697.8116,-373.4777\"/>\n",
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       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>19</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M739.5,-261C739.5,-261 631.5,-261 631.5,-261 625.5,-261 619.5,-255 619.5,-249 619.5,-249 619.5,-205 619.5,-205 619.5,-199 625.5,-193 631.5,-193 631.5,-193 739.5,-193 739.5,-193 745.5,-193 751.5,-199 751.5,-205 751.5,-205 751.5,-249 751.5,-249 751.5,-255 745.5,-261 739.5,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"685.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Revenue &lt;= 44.26</text>\n",
       "<text text-anchor=\"middle\" x=\"685.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 23</text>\n",
       "<text text-anchor=\"middle\" x=\"685.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [11, 12]</text>\n",
       "<text text-anchor=\"middle\" x=\"685.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 18&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>18&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M733.9245,-296.9465C727.8329,-288.1475 721.2004,-278.5672 714.8534,-269.3993\"/>\n",
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       "<!-- 26 -->\n",
       "<g id=\"node27\" class=\"node\">\n",
       "<title>26</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M877,-253.5C877,-253.5 782,-253.5 782,-253.5 776,-253.5 770,-247.5 770,-241.5 770,-241.5 770,-212.5 770,-212.5 770,-206.5 776,-200.5 782,-200.5 782,-200.5 877,-200.5 877,-200.5 883,-200.5 889,-206.5 889,-212.5 889,-212.5 889,-241.5 889,-241.5 889,-247.5 883,-253.5 877,-253.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"829.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8</text>\n",
       "<text text-anchor=\"middle\" x=\"829.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [8, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"829.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 18&#45;&gt;26 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>18&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M781.0755,-296.9465C788.8537,-285.7113 797.5139,-273.2021 805.3355,-261.9043\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"808.3102,-263.7564 811.1247,-253.5422 802.5548,-259.7719 808.3102,-263.7564\"/>\n",
       "</g>\n",
       "<!-- 20 -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>20</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M667,-157C667,-157 546,-157 546,-157 540,-157 534,-151 534,-145 534,-145 534,-101 534,-101 534,-95 540,-89 546,-89 546,-89 667,-89 667,-89 673,-89 679,-95 679,-101 679,-101 679,-145 679,-145 679,-151 673,-157 667,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"606.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Employees &lt;= 264.5</text>\n",
       "<text text-anchor=\"middle\" x=\"606.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 13</text>\n",
       "<text text-anchor=\"middle\" x=\"606.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3, 10]</text>\n",
       "<text text-anchor=\"middle\" x=\"606.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>19&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M659.6324,-192.9465C652.8804,-184.0578 645.5226,-174.3716 638.4941,-165.1188\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"641.2617,-162.976 632.4257,-157.13 635.6875,-167.2102 641.2617,-162.976\"/>\n",
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       "<!-- 23 -->\n",
       "<g id=\"node24\" class=\"node\">\n",
       "<title>23</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M821.5,-157C821.5,-157 709.5,-157 709.5,-157 703.5,-157 697.5,-151 697.5,-145 697.5,-145 697.5,-101 697.5,-101 697.5,-95 703.5,-89 709.5,-89 709.5,-89 821.5,-89 821.5,-89 827.5,-89 833.5,-95 833.5,-101 833.5,-101 833.5,-145 833.5,-145 833.5,-151 827.5,-157 821.5,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"765.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Total_Freq &lt;= 11.5</text>\n",
       "<text text-anchor=\"middle\" x=\"765.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 10</text>\n",
       "<text text-anchor=\"middle\" x=\"765.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [8, 2]</text>\n",
       "<text text-anchor=\"middle\" x=\"765.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;23 -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>19&#45;&gt;23</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M711.695,-192.9465C718.5325,-184.0578 725.9834,-174.3716 733.101,-165.1188\"/>\n",
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       "</g>\n",
       "<!-- 21 -->\n",
       "<g id=\"node22\" class=\"node\">\n",
       "<title>21</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M535,-53C535,-53 460,-53 460,-53 454,-53 448,-47 448,-41 448,-41 448,-12 448,-12 448,-6 454,0 460,0 460,0 535,0 535,0 541,0 547,-6 547,-12 547,-12 547,-41 547,-41 547,-47 541,-53 535,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"497.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"497.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"497.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 20&#45;&gt;21 -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>20&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M568.0707,-88.9777C557.4173,-79.546 545.8641,-69.3178 535.2499,-59.9208\"/>\n",
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       "</g>\n",
       "<!-- 22 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>22</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M652,-53C652,-53 577,-53 577,-53 571,-53 565,-47 565,-41 565,-41 565,-12 565,-12 565,-6 571,0 577,0 577,0 652,0 652,0 658,0 664,-6 664,-12 664,-12 664,-41 664,-41 664,-47 658,-53 652,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"614.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8</text>\n",
       "<text text-anchor=\"middle\" x=\"614.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 7]</text>\n",
       "<text text-anchor=\"middle\" x=\"614.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 20&#45;&gt;22 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>20&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M609.3205,-88.9777C610.0113,-80.6449 610.7537,-71.6903 611.4545,-63.2364\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"614.9443,-63.5035 612.2825,-53.2485 607.9682,-62.9251 614.9443,-63.5035\"/>\n",
       "</g>\n",
       "<!-- 24 -->\n",
       "<g id=\"node25\" class=\"node\">\n",
       "<title>24</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M805,-53C805,-53 710,-53 710,-53 704,-53 698,-47 698,-41 698,-41 698,-12 698,-12 698,-6 704,0 710,0 710,0 805,0 805,0 811,0 817,-6 817,-12 817,-12 817,-41 817,-41 817,-47 811,-53 805,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"757.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"757.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3, 2]</text>\n",
       "<text text-anchor=\"middle\" x=\"757.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 23&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>23&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M762.6795,-88.9777C761.9887,-80.6449 761.2463,-71.6903 760.5455,-63.2364\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"764.0318,-62.9251 759.7175,-53.2485 757.0557,-63.5035 764.0318,-62.9251\"/>\n",
       "</g>\n",
       "<!-- 25 -->\n",
       "<g id=\"node26\" class=\"node\">\n",
       "<title>25</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M942,-53C942,-53 847,-53 847,-53 841,-53 835,-47 835,-41 835,-41 835,-12 835,-12 835,-6 841,0 847,0 847,0 942,0 942,0 948,0 954,-6 954,-12 954,-12 954,-41 954,-41 954,-47 948,-53 942,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"894.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"894.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"894.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 23&#45;&gt;25 -->\n",
       "<g id=\"edge25\" class=\"edge\">\n",
       "<title>23&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M810.9806,-88.9777C823.8335,-79.3629 837.7931,-68.9203 850.554,-59.3743\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"852.832,-62.0412 858.743,-53.2485 848.639,-56.436 852.832,-62.0412\"/>\n",
       "</g>\n",
       "<!-- 29 -->\n",
       "<g id=\"node30\" class=\"node\">\n",
       "<title>29</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M914,-461.5C914,-461.5 839,-461.5 839,-461.5 833,-461.5 827,-455.5 827,-449.5 827,-449.5 827,-420.5 827,-420.5 827,-414.5 833,-408.5 839,-408.5 839,-408.5 914,-408.5 914,-408.5 920,-408.5 926,-414.5 926,-420.5 926,-420.5 926,-449.5 926,-449.5 926,-455.5 920,-461.5 914,-461.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"876.5\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 6</text>\n",
       "<text text-anchor=\"middle\" x=\"876.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 6]</text>\n",
       "<text text-anchor=\"middle\" x=\"876.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 28&#45;&gt;29 -->\n",
       "<g id=\"edge29\" class=\"edge\">\n",
       "<title>28&#45;&gt;29</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M893.3141,-504.9465C890.7457,-494.2621 887.9003,-482.4254 885.2919,-471.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"888.6207,-470.4471 882.8803,-461.5422 881.8146,-472.0833 888.6207,-470.4471\"/>\n",
       "</g>\n",
       "<!-- 30 -->\n",
       "<g id=\"node31\" class=\"node\">\n",
       "<title>30</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1031,-461.5C1031,-461.5 956,-461.5 956,-461.5 950,-461.5 944,-455.5 944,-449.5 944,-449.5 944,-420.5 944,-420.5 944,-414.5 950,-408.5 956,-408.5 956,-408.5 1031,-408.5 1031,-408.5 1037,-408.5 1043,-414.5 1043,-420.5 1043,-420.5 1043,-449.5 1043,-449.5 1043,-455.5 1037,-461.5 1031,-461.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"993.5\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"993.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 4]</text>\n",
       "<text text-anchor=\"middle\" x=\"993.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 28&#45;&gt;30 -->\n",
       "<g id=\"edge30\" class=\"edge\">\n",
       "<title>28&#45;&gt;30</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M931.6242,-504.9465C941.758,-493.491 953.0633,-480.711 963.2097,-469.2412\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"966.0161,-471.3512 970.0204,-461.5422 960.7731,-466.7131 966.0161,-471.3512\"/>\n",
       "</g>\n",
       "<!-- 32 -->\n",
       "<g id=\"node33\" class=\"node\">\n",
       "<title>32</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1366,-677C1366,-677 1237,-677 1237,-677 1231,-677 1225,-671 1225,-665 1225,-665 1225,-621 1225,-621 1225,-615 1231,-609 1237,-609 1237,-609 1366,-609 1366,-609 1372,-609 1378,-615 1378,-621 1378,-621 1378,-665 1378,-665 1378,-671 1372,-677 1366,-677\"/>\n",
       "<text text-anchor=\"middle\" x=\"1301.5\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Employees &lt;= 1119.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1301.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 64</text>\n",
       "<text text-anchor=\"middle\" x=\"1301.5\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [40, 24]</text>\n",
       "<text text-anchor=\"middle\" x=\"1301.5\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 31&#45;&gt;32 -->\n",
       "<g id=\"edge32\" class=\"edge\">\n",
       "<title>31&#45;&gt;32</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1301.5,-712.9465C1301.5,-704.776 1301.5,-695.9318 1301.5,-687.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1305.0001,-687.13 1301.5,-677.13 1298.0001,-687.13 1305.0001,-687.13\"/>\n",
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       "<!-- 45 -->\n",
       "<g id=\"node46\" class=\"node\">\n",
       "<title>45</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1526.5,-677C1526.5,-677 1422.5,-677 1422.5,-677 1416.5,-677 1410.5,-671 1410.5,-665 1410.5,-665 1410.5,-621 1410.5,-621 1410.5,-615 1416.5,-609 1422.5,-609 1422.5,-609 1526.5,-609 1526.5,-609 1532.5,-609 1538.5,-615 1538.5,-621 1538.5,-621 1538.5,-665 1538.5,-665 1538.5,-671 1532.5,-677 1526.5,-677\"/>\n",
       "<text text-anchor=\"middle\" x=\"1474.5\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Total_Freq &lt;= 5.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1474.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 127</text>\n",
       "<text text-anchor=\"middle\" x=\"1474.5\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [118, 9]</text>\n",
       "<text text-anchor=\"middle\" x=\"1474.5\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 31&#45;&gt;45 -->\n",
       "<g id=\"edge45\" class=\"edge\">\n",
       "<title>31&#45;&gt;45</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1358.1467,-712.9465C1374.4263,-703.1599 1392.314,-692.4066 1409.077,-682.3294\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1410.9588,-685.282 1417.7261,-677.13 1407.3522,-679.2826 1410.9588,-685.282\"/>\n",
       "</g>\n",
       "<!-- 33 -->\n",
       "<g id=\"node34\" class=\"node\">\n",
       "<title>33</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1217,-573C1217,-573 1096,-573 1096,-573 1090,-573 1084,-567 1084,-561 1084,-561 1084,-517 1084,-517 1084,-511 1090,-505 1096,-505 1096,-505 1217,-505 1217,-505 1223,-505 1229,-511 1229,-517 1229,-517 1229,-561 1229,-561 1229,-567 1223,-573 1217,-573\"/>\n",
       "<text text-anchor=\"middle\" x=\"1156.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Employees &lt;= 698.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1156.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 44</text>\n",
       "<text text-anchor=\"middle\" x=\"1156.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [20, 24]</text>\n",
       "<text text-anchor=\"middle\" x=\"1156.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 32&#45;&gt;33 -->\n",
       "<g id=\"edge33\" class=\"edge\">\n",
       "<title>32&#45;&gt;33</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1254.0216,-608.9465C1240.7523,-599.4293 1226.2085,-588.9978 1212.4965,-579.163\"/>\n",
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       "<!-- 44 -->\n",
       "<g id=\"node45\" class=\"node\">\n",
       "<title>44</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1354,-565.5C1354,-565.5 1259,-565.5 1259,-565.5 1253,-565.5 1247,-559.5 1247,-553.5 1247,-553.5 1247,-524.5 1247,-524.5 1247,-518.5 1253,-512.5 1259,-512.5 1259,-512.5 1354,-512.5 1354,-512.5 1360,-512.5 1366,-518.5 1366,-524.5 1366,-524.5 1366,-553.5 1366,-553.5 1366,-559.5 1360,-565.5 1354,-565.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"1306.5\" y=\"-550.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 20</text>\n",
       "<text text-anchor=\"middle\" x=\"1306.5\" y=\"-535.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [20, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"1306.5\" y=\"-520.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 32&#45;&gt;44 -->\n",
       "<g id=\"edge44\" class=\"edge\">\n",
       "<title>32&#45;&gt;44</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1303.1372,-608.9465C1303.6509,-598.2621 1304.2199,-586.4254 1304.7416,-575.5742\"/>\n",
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       "<!-- 34 -->\n",
       "<g id=\"node35\" class=\"node\">\n",
       "<title>34</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1207.5,-469C1207.5,-469 1073.5,-469 1073.5,-469 1067.5,-469 1061.5,-463 1061.5,-457 1061.5,-457 1061.5,-413 1061.5,-413 1061.5,-407 1067.5,-401 1073.5,-401 1073.5,-401 1207.5,-401 1207.5,-401 1213.5,-401 1219.5,-407 1219.5,-413 1219.5,-413 1219.5,-457 1219.5,-457 1219.5,-463 1213.5,-469 1207.5,-469\"/>\n",
       "<text text-anchor=\"middle\" x=\"1140.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Total_Crossbuy &lt;= 1.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1140.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 13</text>\n",
       "<text text-anchor=\"middle\" x=\"1140.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 12]</text>\n",
       "<text text-anchor=\"middle\" x=\"1140.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 33&#45;&gt;34 -->\n",
       "<g id=\"edge34\" class=\"edge\">\n",
       "<title>33&#45;&gt;34</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1151.261,-504.9465C1149.9902,-496.6863 1148.6134,-487.7374 1147.2827,-479.0875\"/>\n",
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       "</g>\n",
       "<!-- 37 -->\n",
       "<g id=\"node38\" class=\"node\">\n",
       "<title>37</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1383.5,-469C1383.5,-469 1249.5,-469 1249.5,-469 1243.5,-469 1237.5,-463 1237.5,-457 1237.5,-457 1237.5,-413 1237.5,-413 1237.5,-407 1243.5,-401 1249.5,-401 1249.5,-401 1383.5,-401 1383.5,-401 1389.5,-401 1395.5,-407 1395.5,-413 1395.5,-413 1395.5,-457 1395.5,-457 1395.5,-463 1389.5,-469 1383.5,-469\"/>\n",
       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Total_Crossbuy &lt;= 1.5</text>\n",
       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 31</text>\n",
       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [19, 12]</text>\n",
       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 33&#45;&gt;37 -->\n",
       "<g id=\"edge37\" class=\"edge\">\n",
       "<title>33&#45;&gt;37</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1208.89,-504.9465C1223.8082,-495.2497 1240.1864,-484.6039 1255.5662,-474.6069\"/>\n",
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       "</g>\n",
       "<!-- 35 -->\n",
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       "<title>35</title>\n",
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       "<text text-anchor=\"middle\" x=\"1023.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8</text>\n",
       "<text text-anchor=\"middle\" x=\"1023.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 8]</text>\n",
       "<text text-anchor=\"middle\" x=\"1023.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 34&#45;&gt;35 -->\n",
       "<g id=\"edge35\" class=\"edge\">\n",
       "<title>34&#45;&gt;35</title>\n",
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       "<!-- 36 -->\n",
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       "<title>36</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1178,-357.5C1178,-357.5 1103,-357.5 1103,-357.5 1097,-357.5 1091,-351.5 1091,-345.5 1091,-345.5 1091,-316.5 1091,-316.5 1091,-310.5 1097,-304.5 1103,-304.5 1103,-304.5 1178,-304.5 1178,-304.5 1184,-304.5 1190,-310.5 1190,-316.5 1190,-316.5 1190,-345.5 1190,-345.5 1190,-351.5 1184,-357.5 1178,-357.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"1140.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"1140.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1, 4]</text>\n",
       "<text text-anchor=\"middle\" x=\"1140.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 34&#45;&gt;36 -->\n",
       "<g id=\"edge36\" class=\"edge\">\n",
       "<title>34&#45;&gt;36</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1140.5,-400.9465C1140.5,-390.2621 1140.5,-378.4254 1140.5,-367.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1144.0001,-367.5421 1140.5,-357.5422 1137.0001,-367.5422 1144.0001,-367.5421\"/>\n",
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       "<!-- 38 -->\n",
       "<g id=\"node39\" class=\"node\">\n",
       "<title>38</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1295,-357.5C1295,-357.5 1220,-357.5 1220,-357.5 1214,-357.5 1208,-351.5 1208,-345.5 1208,-345.5 1208,-316.5 1208,-316.5 1208,-310.5 1214,-304.5 1220,-304.5 1220,-304.5 1295,-304.5 1295,-304.5 1301,-304.5 1307,-310.5 1307,-316.5 1307,-316.5 1307,-345.5 1307,-345.5 1307,-351.5 1301,-357.5 1295,-357.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"1257.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 9</text>\n",
       "<text text-anchor=\"middle\" x=\"1257.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3, 6]</text>\n",
       "<text text-anchor=\"middle\" x=\"1257.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 37&#45;&gt;38 -->\n",
       "<g id=\"edge38\" class=\"edge\">\n",
       "<title>37&#45;&gt;38</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1297.1812,-400.9465C1290.9324,-389.9316 1283.9888,-377.6922 1277.6793,-366.5703\"/>\n",
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       "<!-- 39 -->\n",
       "<g id=\"node40\" class=\"node\">\n",
       "<title>39</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1445.5,-365C1445.5,-365 1337.5,-365 1337.5,-365 1331.5,-365 1325.5,-359 1325.5,-353 1325.5,-353 1325.5,-309 1325.5,-309 1325.5,-303 1331.5,-297 1337.5,-297 1337.5,-297 1445.5,-297 1445.5,-297 1451.5,-297 1457.5,-303 1457.5,-309 1457.5,-309 1457.5,-353 1457.5,-353 1457.5,-359 1451.5,-365 1445.5,-365\"/>\n",
       "<text text-anchor=\"middle\" x=\"1391.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Revenue &lt;= 45.45</text>\n",
       "<text text-anchor=\"middle\" x=\"1391.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 22</text>\n",
       "<text text-anchor=\"middle\" x=\"1391.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [16, 6]</text>\n",
       "<text text-anchor=\"middle\" x=\"1391.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 37&#45;&gt;39 -->\n",
       "<g id=\"edge39\" class=\"edge\">\n",
       "<title>37&#45;&gt;39</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1341.0578,-400.9465C1347.4032,-392.1475 1354.3121,-382.5672 1360.9236,-373.3993\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1363.8766,-375.2882 1366.887,-365.13 1358.1989,-371.1937 1363.8766,-375.2882\"/>\n",
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       "<!-- 40 -->\n",
       "<g id=\"node41\" class=\"node\">\n",
       "<title>40</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1381,-261C1381,-261 1244,-261 1244,-261 1238,-261 1232,-255 1232,-249 1232,-249 1232,-205 1232,-205 1232,-199 1238,-193 1244,-193 1244,-193 1381,-193 1381,-193 1387,-193 1393,-199 1393,-205 1393,-205 1393,-249 1393,-249 1393,-255 1387,-261 1381,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"1312.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 18.83</text>\n",
       "<text text-anchor=\"middle\" x=\"1312.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 13</text>\n",
       "<text text-anchor=\"middle\" x=\"1312.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [8, 5]</text>\n",
       "<text text-anchor=\"middle\" x=\"1312.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 39&#45;&gt;40 -->\n",
       "<g id=\"edge40\" class=\"edge\">\n",
       "<title>39&#45;&gt;40</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1365.6324,-296.9465C1358.8804,-288.0578 1351.5226,-278.3716 1344.4941,-269.1188\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1347.2617,-266.976 1338.4257,-261.13 1341.6875,-271.2102 1347.2617,-266.976\"/>\n",
       "</g>\n",
       "<!-- 43 -->\n",
       "<g id=\"node44\" class=\"node\">\n",
       "<title>43</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1518,-253.5C1518,-253.5 1423,-253.5 1423,-253.5 1417,-253.5 1411,-247.5 1411,-241.5 1411,-241.5 1411,-212.5 1411,-212.5 1411,-206.5 1417,-200.5 1423,-200.5 1423,-200.5 1518,-200.5 1518,-200.5 1524,-200.5 1530,-206.5 1530,-212.5 1530,-212.5 1530,-241.5 1530,-241.5 1530,-247.5 1524,-253.5 1518,-253.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"1470.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 9</text>\n",
       "<text text-anchor=\"middle\" x=\"1470.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [8, 1]</text>\n",
       "<text text-anchor=\"middle\" x=\"1470.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 39&#45;&gt;43 -->\n",
       "<g id=\"edge43\" class=\"edge\">\n",
       "<title>39&#45;&gt;43</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1417.3676,-296.9465C1425.9857,-285.6012 1435.5906,-272.9567 1444.2383,-261.5724\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1447.0763,-263.6224 1450.3382,-253.5422 1441.5021,-259.3882 1447.0763,-263.6224\"/>\n",
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       "<!-- 41 -->\n",
       "<g id=\"node42\" class=\"node\">\n",
       "<title>41</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1286,-149.5C1286,-149.5 1211,-149.5 1211,-149.5 1205,-149.5 1199,-143.5 1199,-137.5 1199,-137.5 1199,-108.5 1199,-108.5 1199,-102.5 1205,-96.5 1211,-96.5 1211,-96.5 1286,-96.5 1286,-96.5 1292,-96.5 1298,-102.5 1298,-108.5 1298,-108.5 1298,-137.5 1298,-137.5 1298,-143.5 1292,-149.5 1286,-149.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"1248.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 7</text>\n",
       "<text text-anchor=\"middle\" x=\"1248.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3, 4]</text>\n",
       "<text text-anchor=\"middle\" x=\"1248.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 40&#45;&gt;41 -->\n",
       "<g id=\"edge41\" class=\"edge\">\n",
       "<title>40&#45;&gt;41</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1291.544,-192.9465C1284.6978,-181.8215 1277.0829,-169.4473 1270.1843,-158.237\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1273.0555,-156.2244 1264.8336,-149.5422 1267.0939,-159.8931 1273.0555,-156.2244\"/>\n",
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       "<!-- 42 -->\n",
       "<g id=\"node43\" class=\"node\">\n",
       "<title>42</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1423,-149.5C1423,-149.5 1328,-149.5 1328,-149.5 1322,-149.5 1316,-143.5 1316,-137.5 1316,-137.5 1316,-108.5 1316,-108.5 1316,-102.5 1322,-96.5 1328,-96.5 1328,-96.5 1423,-96.5 1423,-96.5 1429,-96.5 1435,-102.5 1435,-108.5 1435,-108.5 1435,-137.5 1435,-137.5 1435,-143.5 1429,-149.5 1423,-149.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"1375.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 6</text>\n",
       "<text text-anchor=\"middle\" x=\"1375.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 1]</text>\n",
       "<text text-anchor=\"middle\" x=\"1375.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 40&#45;&gt;42 -->\n",
       "<g id=\"edge42\" class=\"edge\">\n",
       "<title>40&#45;&gt;42</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1333.1286,-192.9465C1339.8678,-181.8215 1347.3637,-169.4473 1354.1545,-158.237\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1357.2339,-159.9087 1359.4216,-149.5422 1351.2467,-156.2818 1357.2339,-159.9087\"/>\n",
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       "<!-- 46 -->\n",
       "<g id=\"node47\" class=\"node\">\n",
       "<title>46</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1528.5,-573C1528.5,-573 1420.5,-573 1420.5,-573 1414.5,-573 1408.5,-567 1408.5,-561 1408.5,-561 1408.5,-517 1408.5,-517 1408.5,-511 1414.5,-505 1420.5,-505 1420.5,-505 1528.5,-505 1528.5,-505 1534.5,-505 1540.5,-511 1540.5,-517 1540.5,-517 1540.5,-561 1540.5,-561 1540.5,-567 1534.5,-573 1528.5,-573\"/>\n",
       "<text text-anchor=\"middle\" x=\"1474.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Revenue &lt;= 29.17</text>\n",
       "<text text-anchor=\"middle\" x=\"1474.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 31</text>\n",
       "<text text-anchor=\"middle\" x=\"1474.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [25, 6]</text>\n",
       "<text text-anchor=\"middle\" x=\"1474.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 45&#45;&gt;46 -->\n",
       "<g id=\"edge46\" class=\"edge\">\n",
       "<title>45&#45;&gt;46</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1474.5,-608.9465C1474.5,-600.776 1474.5,-591.9318 1474.5,-583.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1478.0001,-583.13 1474.5,-573.13 1471.0001,-583.13 1478.0001,-583.13\"/>\n",
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       "<!-- 51 -->\n",
       "<g id=\"node52\" class=\"node\">\n",
       "<title>51</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1882,-573C1882,-573 1737,-573 1737,-573 1731,-573 1725,-567 1725,-561 1725,-561 1725,-517 1725,-517 1725,-511 1731,-505 1737,-505 1737,-505 1882,-505 1882,-505 1888,-505 1894,-511 1894,-517 1894,-517 1894,-561 1894,-561 1894,-567 1888,-573 1882,-573\"/>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 10.535</text>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 96</text>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [93, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 45&#45;&gt;51 -->\n",
       "<g id=\"edge51\" class=\"edge\">\n",
       "<title>45&#45;&gt;51</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1538.8068,-612.6786C1542.0646,-611.3867 1545.3083,-610.1519 1548.5,-609 1602.6945,-589.4406 1664.9592,-572.5376 1715.0783,-560.2438\"/>\n",
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       "<!-- 47 -->\n",
       "<g id=\"node48\" class=\"node\">\n",
       "<title>47</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1504,-461.5C1504,-461.5 1429,-461.5 1429,-461.5 1423,-461.5 1417,-455.5 1417,-449.5 1417,-449.5 1417,-420.5 1417,-420.5 1417,-414.5 1423,-408.5 1429,-408.5 1429,-408.5 1504,-408.5 1504,-408.5 1510,-408.5 1516,-414.5 1516,-420.5 1516,-420.5 1516,-449.5 1516,-449.5 1516,-455.5 1510,-461.5 1504,-461.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"1466.5\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"1466.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"1466.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
       "</g>\n",
       "<!-- 46&#45;&gt;47 -->\n",
       "<g id=\"edge47\" class=\"edge\">\n",
       "<title>46&#45;&gt;47</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1471.8805,-504.9465C1471.0586,-494.2621 1470.1481,-482.4254 1469.3134,-471.5742\"/>\n",
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       "<!-- 48 -->\n",
       "<g id=\"node49\" class=\"node\">\n",
       "<title>48</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1667,-469C1667,-469 1546,-469 1546,-469 1540,-469 1534,-463 1534,-457 1534,-457 1534,-413 1534,-413 1534,-407 1540,-401 1546,-401 1546,-401 1667,-401 1667,-401 1673,-401 1679,-407 1679,-413 1679,-413 1679,-457 1679,-457 1679,-463 1673,-469 1667,-469\"/>\n",
       "<text text-anchor=\"middle\" x=\"1606.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Employees &lt;= 756.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1606.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 26</text>\n",
       "<text text-anchor=\"middle\" x=\"1606.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [23, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"1606.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 46&#45;&gt;48 -->\n",
       "<g id=\"edge48\" class=\"edge\">\n",
       "<title>46&#45;&gt;48</title>\n",
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       "<!-- 49 -->\n",
       "<g id=\"node50\" class=\"node\">\n",
       "<title>49</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1583,-357.5C1583,-357.5 1488,-357.5 1488,-357.5 1482,-357.5 1476,-351.5 1476,-345.5 1476,-345.5 1476,-316.5 1476,-316.5 1476,-310.5 1482,-304.5 1488,-304.5 1488,-304.5 1583,-304.5 1583,-304.5 1589,-304.5 1595,-310.5 1595,-316.5 1595,-316.5 1595,-345.5 1595,-345.5 1595,-351.5 1589,-357.5 1583,-357.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"1535.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8</text>\n",
       "<text text-anchor=\"middle\" x=\"1535.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"1535.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 48&#45;&gt;49 -->\n",
       "<g id=\"edge49\" class=\"edge\">\n",
       "<title>48&#45;&gt;49</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1583.2519,-400.9465C1575.5818,-389.7113 1567.0418,-377.2021 1559.3289,-365.9043\"/>\n",
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       "<title>50</title>\n",
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       "<text text-anchor=\"middle\" x=\"1672.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 18</text>\n",
       "<text text-anchor=\"middle\" x=\"1672.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [18, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"1672.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 48&#45;&gt;50 -->\n",
       "<g id=\"edge50\" class=\"edge\">\n",
       "<title>48&#45;&gt;50</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1628.1109,-400.9465C1635.171,-389.8215 1643.0239,-377.4473 1650.1381,-366.237\"/>\n",
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       "<!-- 52 -->\n",
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       "<title>52</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1878,-469C1878,-469 1741,-469 1741,-469 1735,-469 1729,-463 1729,-457 1729,-457 1729,-413 1729,-413 1729,-407 1735,-401 1741,-401 1741,-401 1878,-401 1878,-401 1884,-401 1890,-407 1890,-413 1890,-413 1890,-457 1890,-457 1890,-463 1884,-469 1878,-469\"/>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 6.065</text>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 35</text>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [32, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 51&#45;&gt;52 -->\n",
       "<g id=\"edge52\" class=\"edge\">\n",
       "<title>51&#45;&gt;52</title>\n",
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       "<title>57</title>\n",
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       "<text text-anchor=\"middle\" x=\"1967.5\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 61</text>\n",
       "<text text-anchor=\"middle\" x=\"1967.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [61, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"1967.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
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       "<!-- 51&#45;&gt;57 -->\n",
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       "<title>51&#45;&gt;57</title>\n",
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       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 24</text>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [24, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"1809.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
       "</g>\n",
       "<!-- 52&#45;&gt;53 -->\n",
       "<g id=\"edge53\" class=\"edge\">\n",
       "<title>52&#45;&gt;53</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1809.5,-400.9465C1809.5,-390.2621 1809.5,-378.4254 1809.5,-367.5742\"/>\n",
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       "<title>54</title>\n",
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       "<text text-anchor=\"middle\" x=\"1959.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Employees &lt;= 758.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1959.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 11</text>\n",
       "<text text-anchor=\"middle\" x=\"1959.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [8, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"1959.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
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       "<!-- 52&#45;&gt;54 -->\n",
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       "<title>52&#45;&gt;54</title>\n",
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       "<!-- 55 -->\n",
       "<g id=\"node56\" class=\"node\">\n",
       "<title>55</title>\n",
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       "<text text-anchor=\"middle\" x=\"1890.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 6</text>\n",
       "<text text-anchor=\"middle\" x=\"1890.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"1890.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
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       "<!-- 54&#45;&gt;55 -->\n",
       "<g id=\"edge55\" class=\"edge\">\n",
       "<title>54&#45;&gt;55</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1936.9068,-296.9465C1929.4527,-285.7113 1921.1533,-273.2021 1913.6577,-261.9043\"/>\n",
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       "<title>56</title>\n",
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       "<text text-anchor=\"middle\" x=\"2027.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"2027.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"2027.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
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       "<!-- 54&#45;&gt;56 -->\n",
       "<g id=\"edge56\" class=\"edge\">\n",
       "<title>54&#45;&gt;56</title>\n",
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       "<text text-anchor=\"middle\" x=\"1466.5\" y=\"-758.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 64</text>\n",
       "<text text-anchor=\"middle\" x=\"1466.5\" y=\"-743.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 64]</text>\n",
       "<text text-anchor=\"middle\" x=\"1466.5\" y=\"-728.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
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       "<!-- 58&#45;&gt;59 -->\n",
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       "<title>60</title>\n",
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       "<text text-anchor=\"middle\" x=\"1621.5\" y=\"-765.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Avg_Ret_Exp &lt;= 84.615</text>\n",
       "<text text-anchor=\"middle\" x=\"1621.5\" y=\"-750.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 13</text>\n",
       "<text text-anchor=\"middle\" x=\"1621.5\" y=\"-735.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 8]</text>\n",
       "<text text-anchor=\"middle\" x=\"1621.5\" y=\"-720.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = churn</text>\n",
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       "<text text-anchor=\"middle\" x=\"1617.5\" y=\"-624.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = retention</text>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7fc5848cc6d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = export_graphviz(tree, out_file=None, feature_names=predictors, impurity=False,\n",
    "                           class_names=['retention','churn'], rounded=True) \n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render('tree02') # saves tree to a file\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method will be covered in the Week 12 lecture. We can use the following syntax to implement bagging for the classification trees (however, note that bagging is also a special case of random forests)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
       "                                                        class_weight=None,\n",
       "                                                        criterion='entropy',\n",
       "                                                        max_depth=None,\n",
       "                                                        max_features=None,\n",
       "                                                        max_leaf_nodes=None,\n",
       "                                                        min_impurity_decrease=0.0,\n",
       "                                                        min_impurity_split=None,\n",
       "                                                        min_samples_leaf=1,\n",
       "                                                        min_samples_split=2,\n",
       "                                                        min_weight_fraction_leaf=0.0,\n",
       "                                                        presort='deprecated',\n",
       "                                                        random_state=None,\n",
       "                                                        splitter='best'),\n",
       "                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "                  max_samples=1.0, n_estimators=1000, n_jobs=None,\n",
       "                  oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "bag = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'), n_estimators=1000, random_state=1)\n",
    "bag.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method will be covered in the Week 12 lecture. The syntax to implement a random forest classifier is as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=None, max_features=2,\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=5, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                       n_jobs=None, oob_score=False, random_state=1, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import  RandomForestClassifier\n",
    "rf = RandomForestClassifier(criterion='entropy', max_features= 2, min_samples_leaf=5, n_estimators=1000, random_state=1)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To tune the random forest, we should select a parameter that controls the size of the trees (such as the minimum number of observations in a terminal node) and the number of predictors that are sampled as candidate split variables at each node of a tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found by randomised search: {'min_samples_leaf': 1, 'max_features': 6} \n",
      "\n",
      "CPU times: user 1.76 s, sys: 70.8 ms, total: 1.83 s\n",
      "Wall time: 33.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "model = RandomForestClassifier(criterion = 'entropy',  n_estimators=1000)\n",
    "\n",
    "tuning_parameters = {\n",
    "    'min_samples_leaf': [1, 5, 10, 20, 50],\n",
    "    'max_features': np.arange(1, len(predictors)+1),\n",
    "}\n",
    "\n",
    "rf_search = RandomizedSearchCV(model, tuning_parameters, cv = 5, n_iter= 16, return_train_score=False, n_jobs=4)\n",
    "rf_search.fit(X_train, y_train)\n",
    "\n",
    "rf = rf_search.best_estimator_\n",
    "\n",
    "print('Best parameters found by randomised search:', rf_search.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After tuning the random forest, we may want to increase the number of trees (using the n_estimators parameter) to improve accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=None, max_features=6,\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10000,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.n_estimators = 10000\n",
    "rf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a funtion the statlearning module to plot the variable importances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_feature_importance\n",
    "\n",
    "plot_feature_importance(rf, predictors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now implement some \"old\" methods, logistic regression and KNN, for comparison."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "\n",
    "We estimate a [logistic regression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) model with Scikit-Learn as follows:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logit = LogisticRegression(C=1e3)\n",
    "logit.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbours Classifier\n",
    "\n",
    "For the KNN classifier, we need to write a function to select the number of neighbours by cross validation. We use the [<TT>KNeighborsClassifier</TT>](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier) class to fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='mahalanobis',\n",
       "                     metric_params={'V':                 Avg_Ret_Exp     Revenue      Employees  Total_Crossbuy  \\\n",
       "Avg_Ret_Exp     1089.794985  -26.005308     -92.126184       -2.695855   \n",
       "Revenue          -26.005308  266.668850      76.269418       -2.042006   \n",
       "Employees        -92.126184   76.269418  219526.835833      -35.940113   \n",
       "Total_Crossbuy    -2.695855   -2.042006     -35.940113        2.359875   \n",
       "Total_Freq        -0.822244   -2.850140       6.477368       -0.885363   \n",
       "Industry           0.874163   -0.646909     -20.707137        0.002018   \n",
       "\n",
       "                Total_Freq   Industry  \n",
       "Avg_Ret_Exp      -0.822244   0.874163  \n",
       "Revenue          -2.850140  -0.646909  \n",
       "Employees         6.477368 -20.707137  \n",
       "Total_Crossbuy   -0.885363   0.002018  \n",
       "Total_Freq       34.249724   0.046942  \n",
       "Industry          0.046942   0.240094  },\n",
       "                     n_jobs=None, n_neighbors=35, p=2, weights='uniform')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def knn_cv(X_train, y_train):\n",
    "    \n",
    "    neighbours = np.arange(1, 51)\n",
    "    best_score = 0\n",
    "    \n",
    "    for k in neighbours:\n",
    "        knn = KNeighborsClassifier(n_neighbors = k ,  metric='mahalanobis', metric_params={'V': X_train.cov()})\n",
    "        score = np.mean(cross_val_score(knn, X_train, y_train, cv=10, scoring = 'accuracy'))\n",
    "        if score >= best_score:\n",
    "            best = knn\n",
    "            best_score = score\n",
    "    \n",
    "    return best\n",
    "        \n",
    "knn = knn_cv(X_train, y_train) \n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "Decision tree outperforms the other methods when predicting negative cases (true negative rate) but peforms poorly when predicting positive cases (true positive rate).  Bagging and random forest result in the best ROC curves (highest AUC values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Error rate</th>\n",
       "      <th>True Positive Rate</th>\n",
       "      <th>True Negative Rate</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagged Trees</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Error rate  True Positive Rate  True Negative Rate  \\\n",
       "Logistic Regression        0.24               0.804               0.722   \n",
       "KNN                        0.29               0.652               0.759   \n",
       "Decision Tree              0.26               0.804               0.685   \n",
       "Bagged Trees               0.21               0.848               0.741   \n",
       "Random Forest              0.21               0.848               0.741   \n",
       "\n",
       "                       AUC  Precision  \n",
       "Logistic Regression  0.839      0.712  \n",
       "KNN                  0.822      0.698  \n",
       "Decision Tree        0.807      0.685  \n",
       "Bagged Trees         0.880      0.736  \n",
       "Random Forest        0.879      0.736  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, precision_score\n",
    "\n",
    "columns=['Error rate', 'True Positive Rate', 'True Negative Rate', 'AUC', 'Precision']\n",
    "rows=['Logistic Regression', 'KNN', 'Decision Tree', 'Bagged Trees', 'Random Forest']\n",
    "results=pd.DataFrame(0.0, columns=columns, index=rows) \n",
    "\n",
    "methods=[logit, knn, tree, bag, rf]\n",
    "\n",
    "y_prob = np.zeros((len(test), len(rows)))\n",
    "\n",
    "for i, method in enumerate(methods):\n",
    "    \n",
    "    y_pred = method.predict(X_test)\n",
    "    y_prob[:, i] = method.predict_proba(X_test)[:,1]\n",
    "\n",
    "    confusion  = confusion_matrix(y_test, y_pred) \n",
    "\n",
    "    results.iloc[i,0]=  1 - accuracy_score(y_test, y_pred)\n",
    "    results.iloc[i,1]=  confusion[1,1]/np.sum(confusion[1,:])\n",
    "    results.iloc[i,2]=  confusion[0,0]/np.sum(confusion[0,:])\n",
    "    results.iloc[i,3]=  roc_auc_score(y_test, y_prob[:,i])\n",
    "    results.iloc[i,4]=  precision_score(y_test, y_pred)\n",
    "\n",
    "results.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following ROC plot, the **True Positive Rate** (sensitivity) is displayed on the Y axis, while the **True Negative Rate** (specificity) is displayed on the X axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_roc_curves\n",
    "\n",
    "with sns.color_palette(crayon):\n",
    "    fig, ax = plot_roc_curves(y_test, y_prob, labels=pd.Series(rows))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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